Detection of Metacognitive Process by Using Spectrum and Self-Similarity Analysis of EEG Signal in Super Short Time Series

摘要

In this study, we propose the analysis method of the electroencephalograms (EEG) signal in super short time series for detecting metacognition. Our analyses focus on the power spectrum and the self-similarity of the EEG signal. The self-similarity analysis evaluates complexity of the EEG signal as the variance property α. The Wisconsin Card Sorting Test was used as a task in order to encourage metacognition, and the EEG signal during the task is measured. As a result of analysis, the variance property α in the mean of all the electrode points was increased 0.35 when metacognition occurred. Moreover, the power spectrum of high gamma oscillations (65-140 Hz) increased with the variance property α. These results indicate the self-similarity analysis is useful to detect metacognition, and it is suggested that high gamma oscillations are related to metacognition.